Task:
#importing libraries.
import pandas as pd
import plotly.express as px
import plotly.io as pio
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
#importing the data
csv=pd.read_csv("globalterrorismdb_0718dist.csv",encoding='ISO-8859-1')
csv.head(3)
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | country_txt | region | ... | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | NaN | 0 | NaN | 58 | Dominican Republic | 2 | ... | NaN | NaN | NaN | NaN | PGIS | 0 | 0 | 0 | 0 | NaN |
| 1 | 197000000002 | 1970 | 0 | 0 | NaN | 0 | NaN | 130 | Mexico | 1 | ... | NaN | NaN | NaN | NaN | PGIS | 0 | 1 | 1 | 1 | NaN |
| 2 | 197001000001 | 1970 | 1 | 0 | NaN | 0 | NaN | 160 | Philippines | 5 | ... | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
3 rows × 135 columns
#getting the column names
columns=csv.columns.to_list()
print(f"shape of the dataset: {csv.shape[0]} rows x {csv.shape[1]} columns\n\n columns:\n{columns}")
shape of the dataset: 181691 rows x 135 columns columns: ['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended', 'resolution', 'country', 'country_txt', 'region', 'region_txt', 'provstate', 'city', 'latitude', 'longitude', 'specificity', 'vicinity', 'location', 'summary', 'crit1', 'crit2', 'crit3', 'doubtterr', 'alternative', 'alternative_txt', 'multiple', 'success', 'suicide', 'attacktype1', 'attacktype1_txt', 'attacktype2', 'attacktype2_txt', 'attacktype3', 'attacktype3_txt', 'targtype1', 'targtype1_txt', 'targsubtype1', 'targsubtype1_txt', 'corp1', 'target1', 'natlty1', 'natlty1_txt', 'targtype2', 'targtype2_txt', 'targsubtype2', 'targsubtype2_txt', 'corp2', 'target2', 'natlty2', 'natlty2_txt', 'targtype3', 'targtype3_txt', 'targsubtype3', 'targsubtype3_txt', 'corp3', 'target3', 'natlty3', 'natlty3_txt', 'gname', 'gsubname', 'gname2', 'gsubname2', 'gname3', 'gsubname3', 'motive', 'guncertain1', 'guncertain2', 'guncertain3', 'individual', 'nperps', 'nperpcap', 'claimed', 'claimmode', 'claimmode_txt', 'claim2', 'claimmode2', 'claimmode2_txt', 'claim3', 'claimmode3', 'claimmode3_txt', 'compclaim', 'weaptype1', 'weaptype1_txt', 'weapsubtype1', 'weapsubtype1_txt', 'weaptype2', 'weaptype2_txt', 'weapsubtype2', 'weapsubtype2_txt', 'weaptype3', 'weaptype3_txt', 'weapsubtype3', 'weapsubtype3_txt', 'weaptype4', 'weaptype4_txt', 'weapsubtype4', 'weapsubtype4_txt', 'weapdetail', 'nkill', 'nkillus', 'nkillter', 'nwound', 'nwoundus', 'nwoundte', 'property', 'propextent', 'propextent_txt', 'propvalue', 'propcomment', 'ishostkid', 'nhostkid', 'nhostkidus', 'nhours', 'ndays', 'divert', 'kidhijcountry', 'ransom', 'ransomamt', 'ransomamtus', 'ransompaid', 'ransompaidus', 'ransomnote', 'hostkidoutcome', 'hostkidoutcome_txt', 'nreleased', 'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG', 'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related']
Extracting usefull columns.
df=csv[['iyear', 'imonth', 'iday','country_txt','region_txt', 'provstate','latitude', 'longitude', 'city','location', 'summary','success','suicide',
'targtype1_txt','gname','motive','weapdetail','nkill','nwound']]
#Renaming the columns for better understanding.
df.rename(columns={'iyear':'YEAR', 'imonth':'MONTH', 'iday':'DAY','country_txt':'COUNTRY','region_txt':'REGION', 'provstate':'PROVINCE/STATE','latitude':'LATITUDE','longitude':'LONGITUDE', 'city':'CITY','location':'LOCATION', 'summary':'SUMMARY','success': 'SUCCESS','suicide':'SUICIDE',
'targtype1_txt':'TARGET','gname':'GROUP/ORG','motive':'MOTIVE','weapdetail':'WEAPON_TYPE','nkill':'KILLED','nwound':'WOUNDED'},inplace=True)
Checking for null(nan) values and filling them with 0
print(df.isnull().sum())
#Have to replace the 'NaN' values with 0 to make our data compatible with Mapbox.
df[['LONGITUDE','LATITUDE','KILLED','WOUNDED','LOCATION','CITY']]=df[['LONGITUDE','LATITUDE','KILLED','WOUNDED','LOCATION','CITY']].fillna(0)
df.head(3)
YEAR 0 MONTH 0 DAY 0 COUNTRY 0 REGION 0 PROVINCE/STATE 421 LATITUDE 4556 LONGITUDE 4557 CITY 434 LOCATION 126196 SUMMARY 66129 SUCCESS 0 SUICIDE 0 TARGET 0 GROUP/ORG 0 MOTIVE 131130 WEAPON_TYPE 67670 KILLED 10313 WOUNDED 16311 dtype: int64
| YEAR | MONTH | DAY | COUNTRY | REGION | PROVINCE/STATE | LATITUDE | LONGITUDE | CITY | LOCATION | SUMMARY | SUCCESS | SUICIDE | TARGET | GROUP/ORG | MOTIVE | WEAPON_TYPE | KILLED | WOUNDED | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970 | 7 | 2 | Dominican Republic | Central America & Caribbean | NaN | 18.456792 | -69.951164 | Santo Domingo | 0 | NaN | 1 | 0 | Private Citizens & Property | MANO-D | NaN | NaN | 1.0 | 0.0 |
| 1 | 1970 | 0 | 0 | Mexico | North America | Federal | 19.371887 | -99.086624 | Mexico city | 0 | NaN | 1 | 0 | Government (Diplomatic) | 23rd of September Communist League | NaN | NaN | 0.0 | 0.0 |
| 2 | 1970 | 1 | 0 | Philippines | Southeast Asia | Tarlac | 15.478598 | 120.599741 | Unknown | 0 | NaN | 1 | 0 | Journalists & Media | Unknown | NaN | NaN | 1.0 | 0.0 |
df.describe()
| YEAR | MONTH | DAY | LATITUDE | LONGITUDE | SUCCESS | SUICIDE | KILLED | WOUNDED | |
|---|---|---|---|---|---|---|---|---|---|
| count | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 1.816910e+05 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 |
| mean | 2002.638997 | 6.467277 | 15.505644 | 22.909109 | -4.471911e+02 | 0.889598 | 0.036507 | 2.266860 | 2.883296 |
| std | 13.259430 | 3.388303 | 8.814045 | 18.699442 | 2.021946e+05 | 0.313391 | 0.187549 | 11.227057 | 34.309747 |
| min | 1970.000000 | 0.000000 | 0.000000 | -53.154613 | -8.618590e+07 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 1991.000000 | 4.000000 | 8.000000 | 9.518645 | 1.231572e+00 | 1.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 2009.000000 | 6.000000 | 15.000000 | 31.126646 | 4.314357e+01 | 1.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 2014.000000 | 9.000000 | 23.000000 | 34.538561 | 6.835734e+01 | 1.000000 | 0.000000 | 2.000000 | 2.000000 |
| max | 2017.000000 | 12.000000 | 31.000000 | 74.633553 | 1.793667e+02 | 1.000000 | 1.000000 | 1570.000000 | 8191.000000 |
MxRegion=df['REGION'].value_counts().idxmax()
MxTarget=df['TARGET'].value_counts().idxmax()
MxCountry=df['COUNTRY'].value_counts().idxmax()
MxCity=df['CITY'].value_counts().index[1]
MxGroup=df['GROUP/ORG'].value_counts().index[1]
MxYear=df['YEAR'].value_counts().idxmax()
MnRegion=df['REGION'].value_counts().idxmin()
MnCountry=df['COUNTRY'].value_counts().idxmin()
MnGroup=df['GROUP/ORG'].value_counts().index[-1]
MnYear=df['YEAR'].value_counts().idxmin()
df['VICTIMS']=df['KILLED']+df['WOUNDED']
Total_Victims=df['VICTIMS'].sum()
print("Observation:")
print(f"\nFrom 1970 to 2017, there were total 181691 attacks.\nThis attacks resulted in {int(Total_Victims)} casualties.\n{MxTarget} were primary target.\nThe success rate of those attacks was approx 89 %.The highest death toll caused by an attack was 1570.")
print(f"\nMost Attacked Region: {MxRegion}\nMost Attacked Country: {MxCountry}\nMost Attacked City: {MxCity}\nPrimary Target: {MxTarget}\nMost Attacks Carried Out By(Group/Organisation): {MxGroup}\nMost Attacked Year: {MxYear}\n")
print(f"\nLeast Attacked Region: {MnRegion}\nLeast Attacked Country: {MnCountry}\nLeast Attacks Carried Out By(Group/Organisation): {MnGroup}\nLeast Attacked Year: {MnYear}\n")
Observation: From 1970 to 2017, there were total 181691 attacks. This attacks resulted in 935737 casualties. Private Citizens & Property were primary target. The success rate of those attacks was approx 89 %.The highest death toll caused by an attack was 1570. Most Attacked Region: Middle East & North Africa Most Attacked Country: Iraq Most Attacked City: Baghdad Primary Target: Private Citizens & Property Most Attacks Carried Out By(Group/Organisation): Taliban Most Attacked Year: 2014 Least Attacked Region: Australasia & Oceania Least Attacked Country: Vatican City Least Attacks Carried Out By(Group/Organisation): MANO-D Least Attacked Year: 1971
plt.subplots(figsize=(20,6))
x=df['REGION'].value_counts()[:20].index
y=df['REGION'].value_counts()[:20].values
sns.barplot(x,y,palette='hot')
plt.title(' Regional intensity of terrorism(1970-2017)')
plt.xlabel('Regions')
plt.ylabel('Count')
plt.xticks(rotation= 50)
plt.show()
plt.subplots(figsize=(17,6))
x=df['COUNTRY'].value_counts()[:20].index
y=df['COUNTRY'].value_counts()[:20].values
sns.barplot(x,y,palette='hot')
plt.title('Countries affected by terrorism(1970-2017)')
plt.xlabel('Countries')
plt.xticks(rotation=50)
plt.ylabel('Count')
plt.show()
plt.subplots(figsize=(17,6))
x=df['CITY'].value_counts()[1:20].index
y=df['CITY'].value_counts()[1:20].values
sns.barplot(x,y,palette='hot')
plt.title('Countries affected by terrorism(1970-2017)')
plt.xlabel('Countries')
plt.xticks(rotation=50)
plt.ylabel('Count')
plt.show()
plt.subplots(figsize=(17,6))
x=df['YEAR'].value_counts().index
y=df['YEAR'].value_counts().values
sns.barplot(x,y,palette='Reds')
plt.title('YEARLY TARGETINGS')
plt.xlabel('Year')
plt.xticks(rotation=90)
plt.ylabel('Count')
plt.show()
plt.subplots(figsize=(17,6))
x=df['TARGET'].value_counts().values
y=df['TARGET'].value_counts().index
sns.barplot(x,y,palette='hot')
plt.title('VICTIMS OF TERRORISM')
plt.xlabel('Count')
plt.xticks(rotation=90)
plt.ylabel('Countries')
plt.show()
fig= px.scatter_mapbox(df, lon=df['LONGITUDE'], lat=df['LATITUDE'], zoom=0.75, color= df['YEAR'], size=df['VICTIMS'], width= 900, height=600, title='Casualties by Terrorism(1970-2017)',color_continuous_scale=px.colors.cyclical.IceFire)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":60,"l":0,"b":10})
fig.show()
print('The Map above shows the places which are mostly affected by Terrorism. \nPlaces with red marks are high-risk areas for terrorist activity. \nCountries that should be avoided include Iraq, Syria, Nigeria, Afghanistan, and Pakistan.')
The Map above shows the places which are mostly affected by Terrorism. Places with red marks are high-risk areas for terrorist activity. Countries that should be avoided include Iraq, Syria, Nigeria, Afghanistan, and Pakistan.
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